Overview

Dataset statistics

Number of variables22
Number of observations84548
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.2 MiB
Average record size in memory176.0 B

Variable types

Numeric8
Categorical14

Warnings

EASE-MENT has constant value " " Constant
NEIGHBORHOOD has a high cardinality: 254 distinct values High cardinality
BUILDING CLASS AT PRESENT has a high cardinality: 167 distinct values High cardinality
ADDRESS has a high cardinality: 67563 distinct values High cardinality
APARTMENT NUMBER has a high cardinality: 3989 distinct values High cardinality
LAND SQUARE FEET has a high cardinality: 6062 distinct values High cardinality
GROSS SQUARE FEET has a high cardinality: 5691 distinct values High cardinality
BUILDING CLASS AT TIME OF SALE has a high cardinality: 166 distinct values High cardinality
SALE PRICE has a high cardinality: 10008 distinct values High cardinality
SALE DATE has a high cardinality: 364 distinct values High cardinality
TAX CLASS AT PRESENT is highly correlated with EASE-MENT and 1 other fieldsHigh correlation
EASE-MENT is highly correlated with TAX CLASS AT PRESENT and 3 other fieldsHigh correlation
BOROUGH is highly correlated with EASE-MENTHigh correlation
TAX CLASS AT TIME OF SALE is highly correlated with TAX CLASS AT PRESENT and 2 other fieldsHigh correlation
BUILDING CLASS CATEGORY is highly correlated with EASE-MENT and 1 other fieldsHigh correlation
RESIDENTIAL UNITS is highly skewed (γ1 = 60.70273283) Skewed
COMMERCIAL UNITS is highly skewed (γ1 = 214.4011234) Skewed
TOTAL UNITS is highly skewed (γ1 = 63.44833684) Skewed
ZIP CODE has 982 (1.2%) zeros Zeros
RESIDENTIAL UNITS has 24783 (29.3%) zeros Zeros
COMMERCIAL UNITS has 79429 (93.9%) zeros Zeros
TOTAL UNITS has 19762 (23.4%) zeros Zeros
YEAR BUILT has 6970 (8.2%) zeros Zeros

Reproduction

Analysis started2021-01-26 16:04:16.180396
Analysis finished2021-01-26 16:04:45.894219
Duration29.71 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

Distinct26736
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10344.35988
Minimum4
Maximum26739
Zeros0
Zeros (%)0.0%
Memory size660.7 KiB
2021-01-26T23:04:46.036839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile849
Q14231
median8942
Q315987.25
95-th percentile23281
Maximum26739
Range26735
Interquartile range (IQR)11756.25

Descriptive statistics

Standard deviation7151.779436
Coefficient of variation (CV)0.6913699369
Kurtosis-0.9282200569
Mean10344.35988
Median Absolute Deviation (MAD)5586.5
Skewness0.4407807646
Sum874594939
Variance51147949.11
MonotocityNot monotonic
2021-01-26T23:04:46.219467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20495
 
< 0.1%
4785
 
< 0.1%
3505
 
< 0.1%
23995
 
< 0.1%
45085
 
< 0.1%
65575
 
< 0.1%
4145
 
< 0.1%
24635
 
< 0.1%
45725
 
< 0.1%
66215
 
< 0.1%
Other values (26726)84498
99.9%
ValueCountFrequency (%)
45
< 0.1%
55
< 0.1%
65
< 0.1%
75
< 0.1%
85
< 0.1%
ValueCountFrequency (%)
267391
< 0.1%
267381
< 0.1%
267371
< 0.1%
267361
< 0.1%
267351
< 0.1%

BOROUGH
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
4
26736 
3
24047 
1
18306 
5
8410 
2
7049 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84548
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
426736
31.6%
324047
28.4%
118306
21.7%
58410
 
9.9%
27049
 
8.3%
2021-01-26T23:04:46.511683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-26T23:04:46.600678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
426736
31.6%
324047
28.4%
118306
21.7%
58410
 
9.9%
27049
 
8.3%

Most occurring characters

ValueCountFrequency (%)
426736
31.6%
324047
28.4%
118306
21.7%
58410
 
9.9%
27049
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84548
100.0%

Most frequent character per category

ValueCountFrequency (%)
426736
31.6%
324047
28.4%
118306
21.7%
58410
 
9.9%
27049
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common84548
100.0%

Most frequent character per script

ValueCountFrequency (%)
426736
31.6%
324047
28.4%
118306
21.7%
58410
 
9.9%
27049
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII84548
100.0%

Most frequent character per block

ValueCountFrequency (%)
426736
31.6%
324047
28.4%
118306
21.7%
58410
 
9.9%
27049
 
8.3%

NEIGHBORHOOD
Categorical

HIGH CARDINALITY

Distinct254
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
FLUSHING-NORTH
 
3068
UPPER EAST SIDE (59-79)
 
1736
UPPER EAST SIDE (79-96)
 
1590
UPPER WEST SIDE (59-79)
 
1439
BEDFORD STUYVESANT
 
1436
Other values (249)
75279 

Length

Max length25
Median length12
Mean length13.14498273
Min length4

Characters and Unicode

Total characters1111382
Distinct characters38
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowALPHABET CITY
2nd rowALPHABET CITY
3rd rowALPHABET CITY
4th rowALPHABET CITY
5th rowALPHABET CITY
ValueCountFrequency (%)
FLUSHING-NORTH3068
 
3.6%
UPPER EAST SIDE (59-79)1736
 
2.1%
UPPER EAST SIDE (79-96)1590
 
1.9%
UPPER WEST SIDE (59-79)1439
 
1.7%
BEDFORD STUYVESANT1436
 
1.7%
MIDTOWN EAST1418
 
1.7%
BOROUGH PARK1245
 
1.5%
ASTORIA1216
 
1.4%
BAYSIDE1150
 
1.4%
FOREST HILLS1069
 
1.3%
Other values (244)69181
81.8%
2021-01-26T23:04:46.889292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east6664
 
4.4%
side6484
 
4.3%
upper6471
 
4.3%
park6273
 
4.1%
heights4268
 
2.8%
west4034
 
2.7%
59-793175
 
2.1%
flushing-north3068
 
2.0%
hill2695
 
1.8%
bay2646
 
1.7%
Other values (285)106120
69.9%

Most occurring characters

ValueCountFrequency (%)
E104344
 
9.4%
A80142
 
7.2%
S79590
 
7.2%
R72558
 
6.5%
67350
 
6.1%
I64910
 
5.8%
O62467
 
5.6%
T62184
 
5.6%
L61705
 
5.6%
N60529
 
5.4%
Other values (28)395603
35.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter983670
88.5%
Space Separator67350
 
6.1%
Decimal Number25141
 
2.3%
Dash Punctuation19371
 
1.7%
Open Punctuation6182
 
0.6%
Close Punctuation6182
 
0.6%
Other Punctuation3486
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
E104344
 
10.6%
A80142
 
8.1%
S79590
 
8.1%
R72558
 
7.4%
I64910
 
6.6%
O62467
 
6.4%
T62184
 
6.3%
L61705
 
6.3%
N60529
 
6.2%
H51508
 
5.2%
Other values (16)283733
28.8%
ValueCountFrequency (%)
911951
47.5%
75769
22.9%
63365
 
13.4%
53175
 
12.6%
1826
 
3.3%
055
 
0.2%
ValueCountFrequency (%)
/2230
64.0%
.1256
36.0%
ValueCountFrequency (%)
67350
100.0%
ValueCountFrequency (%)
-19371
100.0%
ValueCountFrequency (%)
(6182
100.0%
ValueCountFrequency (%)
)6182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin983670
88.5%
Common127712
 
11.5%

Most frequent character per script

ValueCountFrequency (%)
E104344
 
10.6%
A80142
 
8.1%
S79590
 
8.1%
R72558
 
7.4%
I64910
 
6.6%
O62467
 
6.4%
T62184
 
6.3%
L61705
 
6.3%
N60529
 
6.2%
H51508
 
5.2%
Other values (16)283733
28.8%
ValueCountFrequency (%)
67350
52.7%
-19371
 
15.2%
911951
 
9.4%
(6182
 
4.8%
)6182
 
4.8%
75769
 
4.5%
63365
 
2.6%
53175
 
2.5%
/2230
 
1.7%
.1256
 
1.0%
Other values (2)881
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1111382
100.0%

Most frequent character per block

ValueCountFrequency (%)
E104344
 
9.4%
A80142
 
7.2%
S79590
 
7.2%
R72558
 
6.5%
67350
 
6.1%
I64910
 
5.8%
O62467
 
5.6%
T62184
 
5.6%
L61705
 
5.6%
N60529
 
5.4%
Other values (28)395603
35.6%

BUILDING CLASS CATEGORY
Categorical

HIGH CORRELATION

Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
01 ONE FAMILY DWELLINGS
18235 
02 TWO FAMILY DWELLINGS
15828 
13 CONDOS - ELEVATOR APARTMENTS
12989 
10 COOPS - ELEVATOR APARTMENTS
12902 
03 THREE FAMILY DWELLINGS
4384 
Other values (42)
20210 

Length

Max length44
Median length43
Mean length43.00050859
Min length43

Characters and Unicode

Total characters3635607
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07 RENTALS - WALKUP APARTMENTS
2nd row07 RENTALS - WALKUP APARTMENTS
3rd row07 RENTALS - WALKUP APARTMENTS
4th row07 RENTALS - WALKUP APARTMENTS
5th row07 RENTALS - WALKUP APARTMENTS
ValueCountFrequency (%)
01 ONE FAMILY DWELLINGS 18235
21.6%
02 TWO FAMILY DWELLINGS 15828
18.7%
13 CONDOS - ELEVATOR APARTMENTS 12989
15.4%
10 COOPS - ELEVATOR APARTMENTS 12902
15.3%
03 THREE FAMILY DWELLINGS 4384
 
5.2%
07 RENTALS - WALKUP APARTMENTS 3466
 
4.1%
09 COOPS - WALKUP APARTMENTS 2767
 
3.3%
04 TAX CLASS 1 CONDOS 1656
 
2.0%
44 CONDO PARKING 1441
 
1.7%
15 CONDOS - 2-10 UNIT RESIDENTIAL 1281
 
1.5%
Other values (37)9599
11.4%
2021-01-26T23:04:47.236127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dwellings38447
 
10.3%
family38447
 
10.3%
35824
 
9.6%
apartments33432
 
8.9%
elevator26273
 
7.0%
one18235
 
4.9%
0118235
 
4.9%
condos16978
 
4.5%
coops16870
 
4.5%
two15828
 
4.2%
Other values (102)115289
30.8%

Most occurring characters

ValueCountFrequency (%)
1746496
48.0%
E165470
 
4.6%
L163874
 
4.5%
A162055
 
4.5%
O141686
 
3.9%
T129584
 
3.6%
N127409
 
3.5%
S125167
 
3.4%
I90952
 
2.5%
R76041
 
2.1%
Other values (26)706873
19.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator1746496
48.0%
Uppercase Letter1672138
46.0%
Decimal Number178488
 
4.9%
Dash Punctuation38292
 
1.1%
Other Punctuation193
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
E165470
 
9.9%
L163874
 
9.8%
A162055
 
9.7%
O141686
 
8.5%
T129584
 
7.7%
N127409
 
7.6%
S125167
 
7.5%
I90952
 
5.4%
R76041
 
4.5%
M74296
 
4.4%
Other values (13)415604
24.9%
ValueCountFrequency (%)
063426
35.5%
154500
30.5%
221413
 
12.0%
319069
 
10.7%
47517
 
4.2%
75345
 
3.0%
93386
 
1.9%
52784
 
1.6%
6560
 
0.3%
8488
 
0.3%
ValueCountFrequency (%)
1746496
100.0%
ValueCountFrequency (%)
-38292
100.0%
ValueCountFrequency (%)
/193
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1963469
54.0%
Latin1672138
46.0%

Most frequent character per script

ValueCountFrequency (%)
E165470
 
9.9%
L163874
 
9.8%
A162055
 
9.7%
O141686
 
8.5%
T129584
 
7.7%
N127409
 
7.6%
S125167
 
7.5%
I90952
 
5.4%
R76041
 
4.5%
M74296
 
4.4%
Other values (13)415604
24.9%
ValueCountFrequency (%)
1746496
88.9%
063426
 
3.2%
154500
 
2.8%
-38292
 
2.0%
221413
 
1.1%
319069
 
1.0%
47517
 
0.4%
75345
 
0.3%
93386
 
0.2%
52784
 
0.1%
Other values (3)1241
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3635607
100.0%

Most frequent character per block

ValueCountFrequency (%)
1746496
48.0%
E165470
 
4.6%
L163874
 
4.5%
A162055
 
4.5%
O141686
 
3.9%
T129584
 
3.6%
N127409
 
3.5%
S125167
 
3.4%
I90952
 
2.5%
R76041
 
2.1%
Other values (26)706873
19.4%

TAX CLASS AT PRESENT
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
1
38633 
2
30919 
4
6140 
2A
 
2521
2C
 
1915
Other values (6)
4420 

Length

Max length2
Median length1
Mean length1.095969154
Min length1

Characters and Unicode

Total characters92662
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2A
2nd row2
3rd row2
4th row2B
5th row2A
ValueCountFrequency (%)
138633
45.7%
230919
36.6%
46140
 
7.3%
2A2521
 
3.0%
2C1915
 
2.3%
1A1444
 
1.7%
1B1234
 
1.5%
2B814
 
1.0%
738
 
0.9%
1C186
 
0.2%
2021-01-26T23:04:47.515887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
138633
46.1%
230919
36.9%
46140
 
7.3%
2a2521
 
3.0%
2c1915
 
2.3%
1a1444
 
1.7%
1b1234
 
1.5%
2b814
 
1.0%
1c186
 
0.2%
34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
141497
44.8%
236169
39.0%
46140
 
6.6%
A3965
 
4.3%
C2101
 
2.3%
B2048
 
2.2%
738
 
0.8%
34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83810
90.4%
Uppercase Letter8114
 
8.8%
Space Separator738
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
141497
49.5%
236169
43.2%
46140
 
7.3%
34
 
< 0.1%
ValueCountFrequency (%)
A3965
48.9%
C2101
25.9%
B2048
25.2%
ValueCountFrequency (%)
738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common84548
91.2%
Latin8114
 
8.8%

Most frequent character per script

ValueCountFrequency (%)
141497
49.1%
236169
42.8%
46140
 
7.3%
738
 
0.9%
34
 
< 0.1%
ValueCountFrequency (%)
A3965
48.9%
C2101
25.9%
B2048
25.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII92662
100.0%

Most frequent character per block

ValueCountFrequency (%)
141497
44.8%
236169
39.0%
46140
 
6.6%
A3965
 
4.3%
C2101
 
2.3%
B2048
 
2.2%
738
 
0.8%
34
 
< 0.1%

BLOCK
Real number (ℝ≥0)

Distinct11566
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4237.218976
Minimum1
Maximum16322
Zeros0
Zeros (%)0.0%
Memory size660.7 KiB
2021-01-26T23:04:47.743282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile276
Q11322.75
median3311
Q36281
95-th percentile11615.65
Maximum16322
Range16321
Interquartile range (IQR)4958.25

Descriptive statistics

Standard deviation3568.263407
Coefficient of variation (CV)0.8421239088
Kurtosis0.5968940341
Mean4237.218976
Median Absolute Deviation (MAD)2212
Skewness1.049335039
Sum358248390
Variance12732503.74
MonotocityNot monotonic
2021-01-26T23:04:48.037497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5066404
 
0.5%
16255
 
0.3%
2135211
 
0.2%
4978187
 
0.2%
1171181
 
0.2%
8489170
 
0.2%
1226168
 
0.2%
3944152
 
0.2%
31135
 
0.2%
1129135
 
0.2%
Other values (11556)82550
97.6%
ValueCountFrequency (%)
126
< 0.1%
35
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
163221
 
< 0.1%
163191
 
< 0.1%
163173
< 0.1%
163162
< 0.1%
163152
< 0.1%

LOT
Real number (ℝ≥0)

Distinct2627
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean376.2240148
Minimum1
Maximum9106
Zeros0
Zeros (%)0.0%
Memory size660.7 KiB
2021-01-26T23:04:48.335699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q122
median50
Q31001
95-th percentile1403
Maximum9106
Range9105
Interquartile range (IQR)979

Descriptive statistics

Standard deviation658.136814
Coefficient of variation (CV)1.749321649
Kurtosis24.93765801
Mean376.2240148
Median Absolute Deviation (MAD)38
Skewness3.500679349
Sum31808988
Variance433144.0659
MonotocityNot monotonic
2021-01-26T23:04:48.623928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14125
 
4.9%
20983
 
1.2%
12972
 
1.1%
40935
 
1.1%
23911
 
1.1%
10895
 
1.1%
15894
 
1.1%
29891
 
1.1%
25879
 
1.0%
19874
 
1.0%
Other values (2617)72189
85.4%
ValueCountFrequency (%)
14125
4.9%
2742
 
0.9%
3811
 
1.0%
4685
 
0.8%
5805
 
1.0%
ValueCountFrequency (%)
91061
< 0.1%
90991
< 0.1%
90851
< 0.1%
90811
< 0.1%
90801
< 0.1%

EASE-MENT
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
84548 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84548
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
84548
100.0%
2021-01-26T23:04:48.932108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-26T23:04:49.014914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
84548
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator84548
100.0%

Most frequent character per category

ValueCountFrequency (%)
84548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common84548
100.0%

Most frequent character per script

ValueCountFrequency (%)
84548
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII84548
100.0%

Most frequent character per block

ValueCountFrequency (%)
84548
100.0%

BUILDING CLASS AT PRESENT
Categorical

HIGH CARDINALITY

Distinct167
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
D4
12663 
R4
12482 
A1
6753 
A5
5683 
B2
4923 
Other values (162)
42044 

Length

Max length2
Median length2
Mean length1.991271231
Min length1

Characters and Unicode

Total characters168358
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowC2
2nd rowC7
3rd rowC7
4th rowC4
5th rowC2
ValueCountFrequency (%)
D412663
15.0%
R412482
14.8%
A16753
 
8.0%
A55683
 
6.7%
B24923
 
5.8%
B14749
 
5.6%
C04379
 
5.2%
B33824
 
4.5%
A22821
 
3.3%
C62760
 
3.3%
Other values (157)23511
27.8%
2021-01-26T23:04:49.287541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d412663
15.1%
r412482
14.9%
a16753
 
8.1%
a55683
 
6.8%
b24923
 
5.9%
b14749
 
5.7%
c04379
 
5.2%
b33824
 
4.6%
a22821
 
3.4%
c62760
 
3.3%
Other values (156)22773
27.2%

Most occurring characters

ValueCountFrequency (%)
426151
15.5%
R20291
12.1%
A17872
10.6%
B15514
9.2%
115395
9.1%
D13289
7.9%
210741
6.4%
C10610
6.3%
37128
 
4.2%
06512
 
3.9%
Other values (26)24855
14.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter86498
51.4%
Decimal Number81122
48.2%
Space Separator738
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
R20291
23.5%
A17872
20.7%
B15514
17.9%
D13289
15.4%
C10610
12.3%
S2194
 
2.5%
G1805
 
2.1%
V1700
 
2.0%
K1092
 
1.3%
O348
 
0.4%
Other values (15)1783
 
2.1%
ValueCountFrequency (%)
426151
32.2%
115395
19.0%
210741
13.2%
37128
 
8.8%
06512
 
8.0%
56242
 
7.7%
94812
 
5.9%
63182
 
3.9%
7769
 
0.9%
8190
 
0.2%
ValueCountFrequency (%)
738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin86498
51.4%
Common81860
48.6%

Most frequent character per script

ValueCountFrequency (%)
R20291
23.5%
A17872
20.7%
B15514
17.9%
D13289
15.4%
C10610
12.3%
S2194
 
2.5%
G1805
 
2.1%
V1700
 
2.0%
K1092
 
1.3%
O348
 
0.4%
Other values (15)1783
 
2.1%
ValueCountFrequency (%)
426151
31.9%
115395
18.8%
210741
13.1%
37128
 
8.7%
06512
 
8.0%
56242
 
7.6%
94812
 
5.9%
63182
 
3.9%
7769
 
0.9%
738
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII168358
100.0%

Most frequent character per block

ValueCountFrequency (%)
426151
15.5%
R20291
12.1%
A17872
10.6%
B15514
9.2%
115395
9.1%
D13289
7.9%
210741
6.4%
C10610
6.3%
37128
 
4.2%
06512
 
3.9%
Other values (26)24855
14.8%

ADDRESS
Categorical

HIGH CARDINALITY

Distinct67563
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
131-05 40TH ROAD
 
210
429 KENT AVENUE
 
158
169 WEST 95TH STREET
 
153
131-03 40TH ROAD
 
147
265 STATE STREET
 
127
Other values (67558)
83753 

Length

Max length34
Median length19
Mean length19.26264371
Min length5

Characters and Unicode

Total characters1628618
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62078 ?
Unique (%)73.4%

Sample

1st row153 AVENUE B
2nd row234 EAST 4TH STREET
3rd row197 EAST 3RD STREET
4th row154 EAST 7TH STREET
5th row301 EAST 10TH STREET
ValueCountFrequency (%)
131-05 40TH ROAD210
 
0.2%
429 KENT AVENUE158
 
0.2%
169 WEST 95TH STREET153
 
0.2%
131-03 40TH ROAD147
 
0.2%
265 STATE STREET127
 
0.2%
550 VANDERBILT AVENUE126
 
0.1%
50 WEST STREET115
 
0.1%
39TH AVENUE108
 
0.1%
30 PARK PLACE107
 
0.1%
1809 EMMONS AVENUE103
 
0.1%
Other values (67553)83194
98.4%
2021-01-26T23:04:49.930630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street39956
 
13.7%
avenue24787
 
8.5%
east9802
 
3.4%
west6651
 
2.3%
road3501
 
1.2%
place3055
 
1.1%
ave1656
 
0.6%
park1435
 
0.5%
boulevard1411
 
0.5%
st1410
 
0.5%
Other values (21800)197250
67.8%

Most occurring characters

ValueCountFrequency (%)
236125
14.5%
E191590
 
11.8%
T146406
 
9.0%
R81143
 
5.0%
179718
 
4.9%
A79687
 
4.9%
S77236
 
4.7%
N58986
 
3.6%
252644
 
3.2%
342496
 
2.6%
Other values (35)582587
35.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter948427
58.2%
Decimal Number400922
24.6%
Space Separator236125
 
14.5%
Dash Punctuation25141
 
1.5%
Other Punctuation18003
 
1.1%

Most frequent character per category

ValueCountFrequency (%)
E191590
20.2%
T146406
15.4%
R81143
8.6%
A79687
8.4%
S77236
8.1%
N58986
 
6.2%
H37130
 
3.9%
U34792
 
3.7%
V34168
 
3.6%
O31485
 
3.3%
Other values (16)175804
18.5%
ValueCountFrequency (%)
179718
19.9%
252644
13.1%
342496
10.6%
540828
10.2%
039582
9.9%
437706
9.4%
630373
 
7.6%
727885
 
7.0%
826154
 
6.5%
923536
 
5.9%
ValueCountFrequency (%)
,16730
92.9%
/743
 
4.1%
.457
 
2.5%
#37
 
0.2%
'23
 
0.1%
&7
 
< 0.1%
*6
 
< 0.1%
ValueCountFrequency (%)
236125
100.0%
ValueCountFrequency (%)
-25141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin948427
58.2%
Common680191
41.8%

Most frequent character per script

ValueCountFrequency (%)
E191590
20.2%
T146406
15.4%
R81143
8.6%
A79687
8.4%
S77236
8.1%
N58986
 
6.2%
H37130
 
3.9%
U34792
 
3.7%
V34168
 
3.6%
O31485
 
3.3%
Other values (16)175804
18.5%
ValueCountFrequency (%)
236125
34.7%
179718
 
11.7%
252644
 
7.7%
342496
 
6.2%
540828
 
6.0%
039582
 
5.8%
437706
 
5.5%
630373
 
4.5%
727885
 
4.1%
826154
 
3.8%
Other values (9)66680
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1628618
100.0%

Most frequent character per block

ValueCountFrequency (%)
236125
14.5%
E191590
 
11.8%
T146406
 
9.0%
R81143
 
5.0%
179718
 
4.9%
A79687
 
4.9%
S77236
 
4.7%
N58986
 
3.6%
252644
 
3.2%
342496
 
2.6%
Other values (35)582587
35.8%

APARTMENT NUMBER
Categorical

HIGH CARDINALITY

Distinct3989
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
65496 
4
 
298
3A
 
295
3B
 
275
2
 
275
Other values (3984)
17909 

Length

Max length11
Median length1
Mean length1.34465629
Min length1

Characters and Unicode

Total characters113688
Distinct characters48
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2458 ?
Unique (%)2.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
65496
77.5%
4298
 
0.4%
3A295
 
0.3%
3B275
 
0.3%
2275
 
0.3%
2B272
 
0.3%
2A263
 
0.3%
3263
 
0.3%
1242
 
0.3%
4B228
 
0.3%
Other values (3979)16641
 
19.7%
2021-01-26T23:04:50.458572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4309
 
1.6%
3a295
 
1.5%
2285
 
1.5%
3b275
 
1.4%
2b274
 
1.4%
3270
 
1.4%
2a264
 
1.4%
1248
 
1.3%
4b228
 
1.2%
4a206
 
1.1%
Other values (3810)16605
86.2%

Most occurring characters

ValueCountFrequency (%)
65703
57.8%
16322
 
5.6%
24521
 
4.0%
33568
 
3.1%
43105
 
2.7%
A2640
 
2.3%
52547
 
2.2%
B2430
 
2.1%
02389
 
2.1%
62131
 
1.9%
Other values (38)18332
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator65703
57.8%
Decimal Number28928
25.4%
Uppercase Letter18172
 
16.0%
Dash Punctuation808
 
0.7%
Other Punctuation65
 
0.1%
Math Symbol4
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%
Modifier Symbol2
 
< 0.1%
Lowercase Letter2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A2640
14.5%
B2430
13.4%
C1947
10.7%
P1732
9.5%
D1379
7.6%
E1144
 
6.3%
H1025
 
5.6%
F872
 
4.8%
S839
 
4.6%
G758
 
4.2%
Other values (16)3406
18.7%
ValueCountFrequency (%)
16322
21.9%
24521
15.6%
33568
12.3%
43105
10.7%
52547
8.8%
02389
 
8.3%
62131
 
7.4%
71635
 
5.7%
81478
 
5.1%
91232
 
4.3%
ValueCountFrequency (%)
/43
66.2%
.13
 
20.0%
&7
 
10.8%
#2
 
3.1%
ValueCountFrequency (%)
b1
50.0%
c1
50.0%
ValueCountFrequency (%)
65703
100.0%
ValueCountFrequency (%)
-808
100.0%
ValueCountFrequency (%)
(2
100.0%
ValueCountFrequency (%)
)2
100.0%
ValueCountFrequency (%)
+4
100.0%
ValueCountFrequency (%)
`2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common95514
84.0%
Latin18174
 
16.0%

Most frequent character per script

ValueCountFrequency (%)
A2640
14.5%
B2430
13.4%
C1947
10.7%
P1732
9.5%
D1379
7.6%
E1144
 
6.3%
H1025
 
5.6%
F872
 
4.8%
S839
 
4.6%
G758
 
4.2%
Other values (18)3408
18.8%
ValueCountFrequency (%)
65703
68.8%
16322
 
6.6%
24521
 
4.7%
33568
 
3.7%
43105
 
3.3%
52547
 
2.7%
02389
 
2.5%
62131
 
2.2%
71635
 
1.7%
81478
 
1.5%
Other values (10)2115
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII113688
100.0%

Most frequent character per block

ValueCountFrequency (%)
65703
57.8%
16322
 
5.6%
24521
 
4.0%
33568
 
3.1%
43105
 
2.7%
A2640
 
2.3%
52547
 
2.2%
B2430
 
2.1%
02389
 
2.1%
62131
 
1.9%
Other values (38)18332
 
16.1%

ZIP CODE
Real number (ℝ≥0)

ZEROS

Distinct186
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10731.99161
Minimum0
Maximum11694
Zeros982
Zeros (%)1.2%
Memory size660.7 KiB
2021-01-26T23:04:50.645108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10011
Q110305
median11209
Q311357
95-th percentile11427
Maximum11694
Range11694
Interquartile range (IQR)1052

Descriptive statistics

Standard deviation1290.879147
Coefficient of variation (CV)0.1202832795
Kurtosis52.53929708
Mean10731.99161
Median Absolute Deviation (MAD)206
Skewness-6.656320824
Sum907368427
Variance1666368.973
MonotocityNot monotonic
2021-01-26T23:04:50.842136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103141687
 
2.0%
113541384
 
1.6%
112011324
 
1.6%
112351312
 
1.6%
112341165
 
1.4%
113751144
 
1.4%
103121088
 
1.3%
103061061
 
1.3%
100231053
 
1.2%
100111048
 
1.2%
Other values (176)72282
85.5%
ValueCountFrequency (%)
0982
1.2%
10001204
 
0.2%
10002328
 
0.4%
10003812
1.0%
1000495
 
0.1%
ValueCountFrequency (%)
11694273
0.3%
11693142
 
0.2%
11692157
 
0.2%
11691435
0.5%
11436312
0.4%

RESIDENTIAL UNITS
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct176
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.025263755
Minimum0
Maximum1844
Zeros24783
Zeros (%)29.3%
Memory size660.7 KiB
2021-01-26T23:04:51.076452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum1844
Range1844
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16.72103701
Coefficient of variation (CV)8.256226859
Kurtosis5299.9341
Mean2.025263755
Median Absolute Deviation (MAD)1
Skewness60.70273283
Sum171232
Variance279.5930788
MonotocityNot monotonic
2021-01-26T23:04:51.261809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134722
41.1%
024783
29.3%
216049
19.0%
34608
 
5.5%
41346
 
1.6%
6787
 
0.9%
8332
 
0.4%
5273
 
0.3%
10145
 
0.2%
16122
 
0.1%
Other values (166)1381
 
1.6%
ValueCountFrequency (%)
024783
29.3%
134722
41.1%
216049
19.0%
34608
 
5.5%
41346
 
1.6%
ValueCountFrequency (%)
18442
< 0.1%
16411
< 0.1%
9481
< 0.1%
8941
< 0.1%
8891
< 0.1%

COMMERCIAL UNITS
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1935586886
Minimum0
Maximum2261
Zeros79429
Zeros (%)93.9%
Memory size660.7 KiB
2021-01-26T23:04:51.488717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2261
Range2261
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.713183368
Coefficient of variation (CV)45.01571814
Kurtosis53950.59279
Mean0.1935586886
Median Absolute Deviation (MAD)0
Skewness214.4011234
Sum16365
Variance75.91956441
MonotocityNot monotonic
2021-01-26T23:04:51.677214image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
079429
93.9%
13558
 
4.2%
2817
 
1.0%
3259
 
0.3%
4137
 
0.2%
574
 
0.1%
670
 
0.1%
731
 
< 0.1%
826
 
< 0.1%
920
 
< 0.1%
Other values (45)127
 
0.2%
ValueCountFrequency (%)
079429
93.9%
13558
 
4.2%
2817
 
1.0%
3259
 
0.3%
4137
 
0.2%
ValueCountFrequency (%)
22611
 
< 0.1%
4362
< 0.1%
4222
< 0.1%
3181
 
< 0.1%
2544
< 0.1%

TOTAL UNITS
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct192
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.249183896
Minimum0
Maximum2261
Zeros19762
Zeros (%)23.4%
Memory size660.7 KiB
2021-01-26T23:04:51.853708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum2261
Range2261
Interquartile range (IQR)1

Descriptive statistics

Standard deviation18.97258443
Coefficient of variation (CV)8.435319348
Kurtosis5719.583676
Mean2.249183896
Median Absolute Deviation (MAD)1
Skewness63.44833684
Sum190164
Variance359.95896
MonotocityNot monotonic
2021-01-26T23:04:52.049190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138356
45.4%
019762
23.4%
215914
18.8%
35412
 
6.4%
41498
 
1.8%
6870
 
1.0%
5423
 
0.5%
8374
 
0.4%
10198
 
0.2%
7197
 
0.2%
Other values (182)1544
 
1.8%
ValueCountFrequency (%)
019762
23.4%
138356
45.4%
215914
18.8%
35412
 
6.4%
41498
 
1.8%
ValueCountFrequency (%)
22611
< 0.1%
18662
< 0.1%
16531
< 0.1%
9551
< 0.1%
9021
< 0.1%

LAND SQUARE FEET
Categorical

HIGH CARDINALITY

Distinct6062
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
-
26252 
0
10326 
2000
3919 
2500
 
3470
4000
 
3044
Other values (6057)
37537 

Length

Max length7
Median length4
Mean length3.648672943
Min length1

Characters and Unicode

Total characters308488
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2675 ?
Unique (%)3.2%

Sample

1st row1633
2nd row4616
3rd row2212
4th row2272
5th row2369
ValueCountFrequency (%)
- 26252
31.0%
010326
 
12.2%
20003919
 
4.6%
25003470
 
4.1%
40003044
 
3.6%
18001192
 
1.4%
30001190
 
1.4%
50001009
 
1.2%
2200512
 
0.6%
2400486
 
0.6%
Other values (6052)33148
39.2%
2021-01-26T23:04:52.417438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
26252
31.0%
010326
 
12.2%
20003919
 
4.6%
25003470
 
4.1%
40003044
 
3.6%
18001192
 
1.4%
30001190
 
1.4%
50001009
 
1.2%
2200512
 
0.6%
2400486
 
0.6%
Other values (6052)33148
39.2%

Most occurring characters

ValueCountFrequency (%)
78756
25.5%
078009
25.3%
228715
 
9.3%
-26252
 
8.5%
517835
 
5.8%
117008
 
5.5%
313832
 
4.5%
413603
 
4.4%
89653
 
3.1%
69217
 
3.0%
Other values (2)15608
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number203480
66.0%
Space Separator78756
 
25.5%
Dash Punctuation26252
 
8.5%

Most frequent character per category

ValueCountFrequency (%)
078009
38.3%
228715
 
14.1%
517835
 
8.8%
117008
 
8.4%
313832
 
6.8%
413603
 
6.7%
89653
 
4.7%
69217
 
4.5%
78986
 
4.4%
96622
 
3.3%
ValueCountFrequency (%)
78756
100.0%
ValueCountFrequency (%)
-26252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common308488
100.0%

Most frequent character per script

ValueCountFrequency (%)
78756
25.5%
078009
25.3%
228715
 
9.3%
-26252
 
8.5%
517835
 
5.8%
117008
 
5.5%
313832
 
4.5%
413603
 
4.4%
89653
 
3.1%
69217
 
3.0%
Other values (2)15608
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII308488
100.0%

Most frequent character per block

ValueCountFrequency (%)
78756
25.5%
078009
25.3%
228715
 
9.3%
-26252
 
8.5%
517835
 
5.8%
117008
 
5.5%
313832
 
4.5%
413603
 
4.4%
89653
 
3.1%
69217
 
3.0%
Other values (2)15608
 
5.1%

GROSS SQUARE FEET
Categorical

HIGH CARDINALITY

Distinct5691
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
-
27612 
0
11417 
2400
 
386
1800
 
361
2000
 
359
Other values (5686)
44413 

Length

Max length7
Median length4
Mean length3.595779912
Min length1

Characters and Unicode

Total characters304016
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2365 ?
Unique (%)2.8%

Sample

1st row6440
2nd row18690
3rd row7803
4th row6794
5th row4615
ValueCountFrequency (%)
- 27612
32.7%
011417
 
13.5%
2400386
 
0.5%
1800361
 
0.4%
2000359
 
0.4%
1600346
 
0.4%
1440340
 
0.4%
3000324
 
0.4%
1200295
 
0.3%
1280281
 
0.3%
Other values (5681)42827
50.7%
2021-01-26T23:04:52.842301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27612
32.7%
011417
 
13.5%
2400386
 
0.5%
1800361
 
0.4%
2000359
 
0.4%
1600346
 
0.4%
1440340
 
0.4%
3000324
 
0.4%
1200295
 
0.3%
1280281
 
0.3%
Other values (5681)42827
50.7%

Most occurring characters

ValueCountFrequency (%)
82836
27.2%
045165
14.9%
130980
 
10.2%
228708
 
9.4%
-27612
 
9.1%
415934
 
5.2%
314746
 
4.9%
614576
 
4.8%
814498
 
4.8%
512031
 
4.0%
Other values (2)16930
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number193568
63.7%
Space Separator82836
27.2%
Dash Punctuation27612
 
9.1%

Most frequent character per category

ValueCountFrequency (%)
045165
23.3%
130980
16.0%
228708
14.8%
415934
 
8.2%
314746
 
7.6%
614576
 
7.5%
814498
 
7.5%
512031
 
6.2%
98593
 
4.4%
78337
 
4.3%
ValueCountFrequency (%)
82836
100.0%
ValueCountFrequency (%)
-27612
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common304016
100.0%

Most frequent character per script

ValueCountFrequency (%)
82836
27.2%
045165
14.9%
130980
 
10.2%
228708
 
9.4%
-27612
 
9.1%
415934
 
5.2%
314746
 
4.9%
614576
 
4.8%
814498
 
4.8%
512031
 
4.0%
Other values (2)16930
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII304016
100.0%

Most frequent character per block

ValueCountFrequency (%)
82836
27.2%
045165
14.9%
130980
 
10.2%
228708
 
9.4%
-27612
 
9.1%
415934
 
5.2%
314746
 
4.9%
614576
 
4.8%
814498
 
4.8%
512031
 
4.0%
Other values (2)16930
 
5.6%

YEAR BUILT
Real number (ℝ≥0)

ZEROS

Distinct158
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1789.322976
Minimum0
Maximum2017
Zeros6970
Zeros (%)8.2%
Memory size660.7 KiB
2021-01-26T23:04:53.004867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11920
median1940
Q31965
95-th percentile2013
Maximum2017
Range2017
Interquartile range (IQR)45

Descriptive statistics

Standard deviation537.3449934
Coefficient of variation (CV)0.3003063173
Kurtosis7.146380103
Mean1789.322976
Median Absolute Deviation (MAD)23
Skewness-3.016062029
Sum151283679
Variance288739.642
MonotocityNot monotonic
2021-01-26T23:04:53.236250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06970
 
8.2%
19206045
 
7.1%
19305043
 
6.0%
19254312
 
5.1%
19103585
 
4.2%
19503156
 
3.7%
19602654
 
3.1%
19402456
 
2.9%
19312246
 
2.7%
19551961
 
2.3%
Other values (148)46120
54.5%
ValueCountFrequency (%)
06970
8.2%
11111
 
< 0.1%
16801
 
< 0.1%
180037
 
< 0.1%
18261
 
< 0.1%
ValueCountFrequency (%)
20176
 
< 0.1%
2016794
0.9%
20151470
1.7%
20141232
1.5%
2013743
0.9%

TAX CLASS AT TIME OF SALE
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
1
41533 
2
36726 
4
6285 
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters84548
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2
ValueCountFrequency (%)
141533
49.1%
236726
43.4%
46285
 
7.4%
34
 
< 0.1%
2021-01-26T23:04:53.539831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-26T23:04:53.617623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
141533
49.1%
236726
43.4%
46285
 
7.4%
34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
141533
49.1%
236726
43.4%
46285
 
7.4%
34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84548
100.0%

Most frequent character per category

ValueCountFrequency (%)
141533
49.1%
236726
43.4%
46285
 
7.4%
34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common84548
100.0%

Most frequent character per script

ValueCountFrequency (%)
141533
49.1%
236726
43.4%
46285
 
7.4%
34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII84548
100.0%

Most frequent character per block

ValueCountFrequency (%)
141533
49.1%
236726
43.4%
46285
 
7.4%
34
 
< 0.1%

BUILDING CLASS AT TIME OF SALE
Categorical

HIGH CARDINALITY

Distinct166
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
R4
12989 
D4
12666 
A1
6751 
A5
5671 
B2
4918 
Other values (161)
41553 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters169096
Distinct characters35
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowC2
2nd rowC7
3rd rowC7
4th rowC4
5th rowC2
ValueCountFrequency (%)
R412989
15.4%
D412666
15.0%
A16751
 
8.0%
A55671
 
6.7%
B24918
 
5.8%
B14747
 
5.6%
C04384
 
5.2%
B33821
 
4.5%
A22867
 
3.4%
C62760
 
3.3%
Other values (156)22974
27.2%
2021-01-26T23:04:53.929789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r412989
15.4%
d412666
15.0%
a16751
 
8.0%
a55671
 
6.7%
b24918
 
5.8%
b14747
 
5.6%
c04384
 
5.2%
b33821
 
4.5%
a22867
 
3.4%
c62760
 
3.3%
Other values (156)22974
27.2%

Most occurring characters

ValueCountFrequency (%)
426664
15.8%
R21018
12.4%
A17875
10.6%
B15508
9.2%
115445
9.1%
D13284
7.9%
210784
6.4%
C10617
 
6.3%
37129
 
4.2%
06488
 
3.8%
Other values (25)24284
14.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter87366
51.7%
Decimal Number81730
48.3%

Most frequent character per category

ValueCountFrequency (%)
R21018
24.1%
A17875
20.5%
B15508
17.8%
D13284
15.2%
C10617
12.2%
S2221
 
2.5%
G1873
 
2.1%
V1711
 
2.0%
K1089
 
1.2%
P353
 
0.4%
Other values (15)1817
 
2.1%
ValueCountFrequency (%)
426664
32.6%
115445
18.9%
210784
13.2%
37129
 
8.7%
06488
 
7.9%
56242
 
7.6%
94827
 
5.9%
63198
 
3.9%
7767
 
0.9%
8186
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin87366
51.7%
Common81730
48.3%

Most frequent character per script

ValueCountFrequency (%)
R21018
24.1%
A17875
20.5%
B15508
17.8%
D13284
15.2%
C10617
12.2%
S2221
 
2.5%
G1873
 
2.1%
V1711
 
2.0%
K1089
 
1.2%
P353
 
0.4%
Other values (15)1817
 
2.1%
ValueCountFrequency (%)
426664
32.6%
115445
18.9%
210784
13.2%
37129
 
8.7%
06488
 
7.9%
56242
 
7.6%
94827
 
5.9%
63198
 
3.9%
7767
 
0.9%
8186
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII169096
100.0%

Most frequent character per block

ValueCountFrequency (%)
426664
15.8%
R21018
12.4%
A17875
10.6%
B15508
9.2%
115445
9.1%
D13284
7.9%
210784
6.4%
C10617
 
6.3%
37129
 
4.2%
06488
 
3.8%
Other values (25)24284
14.4%

SALE PRICE
Categorical

HIGH CARDINALITY

Distinct10008
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
-
14561 
0
10228 
10
 
766
450000
 
427
550000
 
416
Other values (10003)
58150 

Length

Max length10
Median length6
Mean length5.176030184
Min length1

Characters and Unicode

Total characters437623
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6916 ?
Unique (%)8.2%

Sample

1st row6625000
2nd row -
3rd row -
4th row3936272
5th row8000000
ValueCountFrequency (%)
- 14561
 
17.2%
010228
 
12.1%
10766
 
0.9%
450000427
 
0.5%
550000416
 
0.5%
650000414
 
0.5%
600000409
 
0.5%
700000382
 
0.5%
400000378
 
0.4%
750000377
 
0.4%
Other values (9998)56190
66.5%
2021-01-26T23:04:54.246941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14561
 
17.2%
010228
 
12.1%
10766
 
0.9%
450000427
 
0.5%
550000416
 
0.5%
650000414
 
0.5%
600000409
 
0.5%
700000382
 
0.5%
400000378
 
0.4%
750000377
 
0.4%
Other values (9998)56190
66.5%

Most occurring characters

ValueCountFrequency (%)
0201287
46.0%
43683
 
10.0%
535469
 
8.1%
124639
 
5.6%
220911
 
4.8%
317749
 
4.1%
416596
 
3.8%
716167
 
3.7%
916072
 
3.7%
615734
 
3.6%
Other values (2)29316
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number379379
86.7%
Space Separator43683
 
10.0%
Dash Punctuation14561
 
3.3%

Most frequent character per category

ValueCountFrequency (%)
0201287
53.1%
535469
 
9.3%
124639
 
6.5%
220911
 
5.5%
317749
 
4.7%
416596
 
4.4%
716167
 
4.3%
916072
 
4.2%
615734
 
4.1%
814755
 
3.9%
ValueCountFrequency (%)
43683
100.0%
ValueCountFrequency (%)
-14561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common437623
100.0%

Most frequent character per script

ValueCountFrequency (%)
0201287
46.0%
43683
 
10.0%
535469
 
8.1%
124639
 
5.6%
220911
 
4.8%
317749
 
4.1%
416596
 
3.8%
716167
 
3.7%
916072
 
3.7%
615734
 
3.6%
Other values (2)29316
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII437623
100.0%

Most frequent character per block

ValueCountFrequency (%)
0201287
46.0%
43683
 
10.0%
535469
 
8.1%
124639
 
5.6%
220911
 
4.8%
317749
 
4.1%
416596
 
3.8%
716167
 
3.7%
916072
 
3.7%
615734
 
3.6%
Other values (2)29316
 
6.7%

SALE DATE
Categorical

HIGH CARDINALITY

Distinct364
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size660.7 KiB
2017-06-29 00:00:00
 
544
2017-06-15 00:00:00
 
530
2016-12-22 00:00:00
 
527
2017-05-25 00:00:00
 
511
2016-10-06 00:00:00
 
508
Other values (359)
81928 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1606412
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2017-07-19 00:00:00
2nd row2016-12-14 00:00:00
3rd row2016-12-09 00:00:00
4th row2016-09-23 00:00:00
5th row2016-11-17 00:00:00
ValueCountFrequency (%)
2017-06-29 00:00:00544
 
0.6%
2017-06-15 00:00:00530
 
0.6%
2016-12-22 00:00:00527
 
0.6%
2017-05-25 00:00:00511
 
0.6%
2016-10-06 00:00:00508
 
0.6%
2017-06-30 00:00:00493
 
0.6%
2016-10-28 00:00:00493
 
0.6%
2017-03-30 00:00:00493
 
0.6%
2016-09-22 00:00:00489
 
0.6%
2016-09-29 00:00:00474
 
0.6%
Other values (354)79486
94.0%
2021-01-26T23:04:54.602558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:0084548
50.0%
2017-06-29544
 
0.3%
2017-06-15530
 
0.3%
2016-12-22527
 
0.3%
2017-05-25511
 
0.3%
2016-10-06508
 
0.3%
2017-03-30493
 
0.3%
2017-06-30493
 
0.3%
2016-10-28493
 
0.3%
2016-09-22489
 
0.3%
Other values (355)79960
47.3%

Most occurring characters

ValueCountFrequency (%)
0693670
43.2%
-169096
 
10.5%
:169096
 
10.5%
1157522
 
9.8%
2135506
 
8.4%
84548
 
5.3%
771062
 
4.4%
646100
 
2.9%
321154
 
1.3%
915627
 
1.0%
Other values (3)43031
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1183672
73.7%
Dash Punctuation169096
 
10.5%
Other Punctuation169096
 
10.5%
Space Separator84548
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
0693670
58.6%
1157522
 
13.3%
2135506
 
11.4%
771062
 
6.0%
646100
 
3.9%
321154
 
1.8%
915627
 
1.3%
514766
 
1.2%
814473
 
1.2%
413792
 
1.2%
ValueCountFrequency (%)
-169096
100.0%
ValueCountFrequency (%)
84548
100.0%
ValueCountFrequency (%)
:169096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1606412
100.0%

Most frequent character per script

ValueCountFrequency (%)
0693670
43.2%
-169096
 
10.5%
:169096
 
10.5%
1157522
 
9.8%
2135506
 
8.4%
84548
 
5.3%
771062
 
4.4%
646100
 
2.9%
321154
 
1.3%
915627
 
1.0%
Other values (3)43031
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1606412
100.0%

Most frequent character per block

ValueCountFrequency (%)
0693670
43.2%
-169096
 
10.5%
:169096
 
10.5%
1157522
 
9.8%
2135506
 
8.4%
84548
 
5.3%
771062
 
4.4%
646100
 
2.9%
321154
 
1.3%
915627
 
1.0%
Other values (3)43031
 
2.7%

Interactions

2021-01-26T23:04:33.211082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:33.456455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:33.608051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:33.757679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:33.906255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:34.075801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:34.233381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:34.392954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:34.581450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:34.751994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:34.976393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:35.193814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:35.403251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:35.577819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:35.758305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:35.922889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:36.082435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:36.246998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:36.396597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:36.551184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:36.709761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:36.872756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:37.019364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:37.174921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:37.414035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-26T23:04:37.761107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-26T23:04:38.851230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:39.008545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:39.226959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:39.513194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:39.684009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:39.949314image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:40.230563image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:40.435275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:40.584903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:40.746440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:40.914020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:41.073568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:41.253088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:41.429615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:41.594175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:41.747796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:41.897394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:42.038986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:42.186590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:42.333226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:42.510978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:42.714433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:42.909912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:43.081453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:43.264964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-26T23:04:43.471442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-26T23:04:54.728226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-26T23:04:54.963295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-26T23:04:55.193679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-26T23:04:55.495870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-26T23:04:55.940682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-26T23:04:44.521603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-26T23:04:45.313535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0BOROUGHNEIGHBORHOODBUILDING CLASS CATEGORYTAX CLASS AT PRESENTBLOCKLOTEASE-MENTBUILDING CLASS AT PRESENTADDRESSAPARTMENT NUMBERZIP CODERESIDENTIAL UNITSCOMMERCIAL UNITSTOTAL UNITSLAND SQUARE FEETGROSS SQUARE FEETYEAR BUILTTAX CLASS AT TIME OF SALEBUILDING CLASS AT TIME OF SALESALE PRICESALE DATE
041ALPHABET CITY07 RENTALS - WALKUP APARTMENTS2A3926C2153 AVENUE B100095051633644019002C266250002017-07-19 00:00:00
151ALPHABET CITY07 RENTALS - WALKUP APARTMENTS239926C7234 EAST 4TH STREET100092833146161869019002C7-2016-12-14 00:00:00
261ALPHABET CITY07 RENTALS - WALKUP APARTMENTS239939C7197 EAST 3RD STREET10009161172212780319002C7-2016-12-09 00:00:00
371ALPHABET CITY07 RENTALS - WALKUP APARTMENTS2B40221C4154 EAST 7TH STREET10009100102272679419132C439362722016-09-23 00:00:00
481ALPHABET CITY07 RENTALS - WALKUP APARTMENTS2A40455C2301 EAST 10TH STREET100096062369461519002C280000002016-11-17 00:00:00
591ALPHABET CITY07 RENTALS - WALKUP APARTMENTS240516C4516 EAST 12TH STREET10009200202581973019002C4-2017-07-20 00:00:00
6101ALPHABET CITY07 RENTALS - WALKUP APARTMENTS2B40632C4210 AVENUE B100098081750422619202C431928402016-09-23 00:00:00
7111ALPHABET CITY07 RENTALS - WALKUP APARTMENTS240718C7520 EAST 14TH STREET100094424651632100719002C7-2017-07-20 00:00:00
8121ALPHABET CITY08 RENTALS - ELEVATOR APARTMENTS237934D5141 AVENUE D10009150151534919819202D5-2017-06-20 00:00:00
9131ALPHABET CITY08 RENTALS - ELEVATOR APARTMENTS2387153D9629 EAST 5TH STREET100092402444891852319202D9162320002016-11-07 00:00:00

Last rows

Unnamed: 0BOROUGHNEIGHBORHOODBUILDING CLASS CATEGORYTAX CLASS AT PRESENTBLOCKLOTEASE-MENTBUILDING CLASS AT PRESENTADDRESSAPARTMENT NUMBERZIP CODERESIDENTIAL UNITSCOMMERCIAL UNITSTOTAL UNITSLAND SQUARE FEETGROSS SQUARE FEETYEAR BUILTTAX CLASS AT TIME OF SALEBUILDING CLASS AT TIME OF SALESALE PRICESALE DATE
8453884045WOODROW02 TWO FAMILY DWELLINGS1731661B2178 DARNELL LANE103092023215130019951B2-2017-06-30 00:00:00
8453984055WOODROW02 TWO FAMILY DWELLINGS1731685B2137 DARNELL LANE103092023016130019951B2-2016-12-30 00:00:00
8454084065WOODROW02 TWO FAMILY DWELLINGS1731693B2125 DARNELL LANE103092023325130019951B25090002016-10-31 00:00:00
8454184075WOODROW02 TWO FAMILY DWELLINGS17317126B2112 ROBIN COURT1030920211088216019941B26480002016-12-07 00:00:00
8454284085WOODROW02 TWO FAMILY DWELLINGS1733941B941 SONIA COURT103092023020180019971B9-2016-12-01 00:00:00
8454384095WOODROW02 TWO FAMILY DWELLINGS1734934B937 QUAIL LANE103092022400257519981B94500002016-11-28 00:00:00
8454484105WOODROW02 TWO FAMILY DWELLINGS1734978B932 PHEASANT LANE103092022498237719981B95500002017-04-21 00:00:00
8454584115WOODROW02 TWO FAMILY DWELLINGS1735160B249 PITNEY AVENUE103092024000149619251B24600002017-07-05 00:00:00
8454684125WOODROW22 STORE BUILDINGS4710028K62730 ARTHUR KILL ROAD103090772080336411720014K6116933372016-12-21 00:00:00
8454784135WOODROW35 INDOOR PUBLIC AND CULTURAL FACILITIES47105679P9155 CLAY PIT ROAD1030901110796240020064P9693002016-10-27 00:00:00